Author: Nithin Mohan TK

Introduction: Testing LLM applications is fundamentally different from testing traditional software. Outputs are non-deterministic, quality is subjective, and edge cases are infinite. You can’t simply assert that output equals expected—you need to evaluate whether outputs are good enough across multiple dimensions. Yet many teams skip testing entirely or rely solely on manual spot-checking. This guide […]

Introduction: Memory is what transforms a stateless LLM into a persistent, context-aware agent. Without memory, every interaction starts from scratch—the agent forgets previous conversations, learned preferences, and accumulated knowledge. But implementing memory for agents is more complex than simply storing chat history. You need short-term memory for the current task, long-term memory for persistent knowledge, […]

Introduction: Getting LLMs to output valid JSON, XML, or other structured formats is surprisingly difficult. Models hallucinate extra fields, forget closing brackets, and produce malformed output that breaks downstream systems. Prompt engineering helps but doesn’t guarantee valid output. This guide covers techniques for reliable structured generation: using native JSON mode and structured outputs, constrained decoding […]

Introduction: LLM API calls are expensive and slow. A single GPT-4 request can cost $0.03-0.12 and take 2-10 seconds. When users ask similar questions repeatedly, you’re paying for the same computation over and over. Caching solves this by storing responses and returning them instantly for matching requests. But LLM caching is harder than traditional caching—users […]

Introduction: Choosing the right embedding model is critical for RAG systems, semantic search, and similarity applications. The wrong choice leads to poor retrieval quality, high costs, or unacceptable latency. OpenAI’s text-embedding-3-small is cheap and fast but may miss nuanced similarities. Cohere’s embed-v3 excels at multilingual content. Open-source models like BGE and E5 offer privacy and […]